Toxicogenomics Case Study: Furan
Bibliographic record
Abstract
Development of pragmatic methodologies for human health risk assessment is required to address current regulatory challenges. We applied three toxicogenomic approaches—quantitative, predictive, and mechanistic—to a case study in mice exposed for 3 weeks to the hepatocarcinogen furan. We modeled the dose response of a variety of transcriptional endpoints and found that they produced benchmark doses similar to the furan-dependent cancer benchmark doses. Meta-analyses showed strong similarity between furan-dependent gene expression changes and those associated with several hepatic pathologies. Molecular pathways facilitated the development of a molecular mode of action for furan-induced hepatocellular carcinogenicity. Finally, we compared transcriptomic profiles derived from formalin-fixed and paraffin-embedded (FFPE) samples with those from high-quality frozen samples to evaluate whether archival samples are a viable option for toxicogenomic studies. The advantage of using FFPE tissues is that they are very well characterized (phenotypically); the disadvantage is that formalin degrades biomacromolecules, including RNA. We found that FFPE samples can be used for toxicogenomics using a ribo-depletion RNA-seq protocol. Our case study demonstrates the utility of toxicogenomics data to human health risk assessment, the potential of archival FFPE tissue samples, and identifies viable strategies toward the reduction of animal usage in chemical testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".